{"title":"QoE Evaluation for VR with Vibrotactile Feedback Based on Inter-user Brain Spatial Information","authors":"Yan Zhang, Rui Song, Riting Xia, Zhenwei Shi","doi":"10.1145/3777459","DOIUrl":null,"url":null,"abstract":"Subjective measurement remains one of the most widely used approaches for evaluating Quality of Experience (QoE) in tactile virtual environments. However, its reliability is often compromised by factors such as conscious bias, variations in user expressiveness, and contextual influences, which may distort the accuracy of evaluation outcomes. In light of the fact that Electroencephalography (EEG) provides a direct window into neurophysiological correlates of emotion and cognitive states, this article proposes an objective QoE evaluation method for Virtual Reality (VR) with vibrotactile feedback based on Inter-user Brain Spatial Information (IBSI). The proposed IBSI feature extraction method enhances the conventional Common Spatial Pattern (CSP) algorithm through the introduction of a regularization constraint term designed to mitigate overfitting. Moreover, the covariance matrix of each non-target user is weighted according to its Kullback–Leibler divergence from the target user, enhancing cross-user alignment and supporting effective transfer of neural information. To validate the performance of the proposed method, we design a VR shooting interaction experiment involving 64 participants. The study comprises three main phases: preparation, interaction, and subjective feedback. During the preparation phase, participants receive task explanations and familiarize themselves with the VR environment. In the interaction phase, participants complete a standardized shooting task, while EEG data are recorded synchronously. Finally, subjective feedback is collected through questionnaires. QoE assessment is accomplished by classifying IBSI features using classical classifiers, with subjective QoE ratings serving as ground truth. Experimental results show that our method outperforms existing methods in classification accuracy, and the dominant activation pattern in the alpha rhythm is consistent with neural mechanisms associated with motor perception. Meanwhile, the mutual interpretability between subjective and objective data characterizing QoE further validates the rationality of the experimental paradigm.","PeriodicalId":50937,"journal":{"name":"ACM Transactions on Multimedia Computing Communications and Applications","volume":"22 1","pages":"1-20"},"PeriodicalIF":5.6000,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Multimedia Computing Communications and Applications","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1145/3777459","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Subjective measurement remains one of the most widely used approaches for evaluating Quality of Experience (QoE) in tactile virtual environments. However, its reliability is often compromised by factors such as conscious bias, variations in user expressiveness, and contextual influences, which may distort the accuracy of evaluation outcomes. In light of the fact that Electroencephalography (EEG) provides a direct window into neurophysiological correlates of emotion and cognitive states, this article proposes an objective QoE evaluation method for Virtual Reality (VR) with vibrotactile feedback based on Inter-user Brain Spatial Information (IBSI). The proposed IBSI feature extraction method enhances the conventional Common Spatial Pattern (CSP) algorithm through the introduction of a regularization constraint term designed to mitigate overfitting. Moreover, the covariance matrix of each non-target user is weighted according to its Kullback–Leibler divergence from the target user, enhancing cross-user alignment and supporting effective transfer of neural information. To validate the performance of the proposed method, we design a VR shooting interaction experiment involving 64 participants. The study comprises three main phases: preparation, interaction, and subjective feedback. During the preparation phase, participants receive task explanations and familiarize themselves with the VR environment. In the interaction phase, participants complete a standardized shooting task, while EEG data are recorded synchronously. Finally, subjective feedback is collected through questionnaires. QoE assessment is accomplished by classifying IBSI features using classical classifiers, with subjective QoE ratings serving as ground truth. Experimental results show that our method outperforms existing methods in classification accuracy, and the dominant activation pattern in the alpha rhythm is consistent with neural mechanisms associated with motor perception. Meanwhile, the mutual interpretability between subjective and objective data characterizing QoE further validates the rationality of the experimental paradigm.
期刊介绍:
The ACM Transactions on Multimedia Computing, Communications, and Applications is the flagship publication of the ACM Special Interest Group in Multimedia (SIGMM). It is soliciting paper submissions on all aspects of multimedia. Papers on single media (for instance, audio, video, animation) and their processing are also welcome.
TOMM is a peer-reviewed, archival journal, available in both print form and digital form. The Journal is published quarterly; with roughly 7 23-page articles in each issue. In addition, all Special Issues are published online-only to ensure a timely publication. The transactions consists primarily of research papers. This is an archival journal and it is intended that the papers will have lasting importance and value over time. In general, papers whose primary focus is on particular multimedia products or the current state of the industry will not be included.